Models & Tools

1. Introduction

In this section we present models and tools aimed to measure, monitor and/or assess multiple facets of biodiversity and ecosystems services. As depicted in the Figure below, this compilation of models and tools operate within a very complex set of interacting systems at different spatial and temporal scales. For instance, the biophysical system and multiple drives (from the socio-economic and cultural systems) shape the ecosystem services that in turn affect human-well-being at local, regional and global scales. Management, governance, actions and interventions (or the lack of them) close the cycle by modifying the intensity, interaction and spatial manifestation of drivers.

Biophysical, social-economic and cultural system framing our search for model and tools (typology for the classification of ecosystem services from Groot et al. 2002). Notice this is a simplified example of some drivers of change, management and governance (hexagons); as well as, some biophysical elements (orange boxes) interacting (black thin arrows) to sustain ecological processes (dark green boxes) that in turn offer ecosystem services (magenta boxes), affecting human well-being (light green boxes). Red arrows describe relationships and feed backs from and to all systems.

“Models” are qualitative or quantitative descriptions of key components of a system and of relationships between those components (IPBES 2016). Tools have been developed to operationalize the systematic use of these models.

Models have been designed to operate within one or multiple socio-economic and/or biophysical components (see Figure below). For instance, some models have a very narrow scope of application such as PROMETHEUS for fires; others tend to integrate more components such as Earth System Models (ESMs) or Integrated Assessment Models (IAMs). Panel (b) in Figure below shows a detailed description of models for biodiversity and physical features.

a) biophysical, economic and socio-cultural systems (adapted from Giupponi et al. 2022), and b) detail of models for biodiversity and physical features. ABM (Agent-Based Model), BPBM (Biophysical Process-Based Model), BRT (Boosted Regression Trees), BurnP3 (spatial fire simulation model), CAM (Cellular Automata Model), CaMP (Canadian Model for Peatlands), CLUE (Conversion of Land Use and its Effects), Circuitscape (connectivity), CSI (Connectivity Status Index-Rivers), CWIM (Canadian Wetland Inventory Map), DGVM (Dynamic Global Vegetation Model), DRSM (Direct Remote Sensing), E2E (End to End Model), Ecopath (aquatic webs), eDNA (Environmental DNA), eFlows (Environmental flows), ESM (Earth System Model), GCM (General Circulation Model), FLUS (Future Land Use Simulation model), GDM (Generalized Dissimilarity Model), GLCM (General Landscape Connectivity Model), Graph theory (connectivity), HYPE (HYdrological Predictions for the Environment), IAMs (Integrated Assessment Models), MBM (Mass Balance Models), Maxent (Maximum Entropy), OM (Occupancy Model), ORCHIDEE-PEAT (Organising Carbon and Hydrology In Dynamic Ecosystems-Peatlands), PBM (Process-Based Model), PLUS (Patch-generating Land Use Simulation), PROMETHEUS (deterministic wildland fire growth simulation model), SDM (Species Distribution Model), SLBM (Site Level Biodiversity metrics), SM (Stratify and Multiply), TOPMODEL (TOPography-based hydrological model), WISE (Wildfire Simulation Engine).

Multiple institutions and initiatives have developed and maintained tools that house models described above. For instance, Bon in A Box a GEO BON global initiative, is in the process of developing multiple data-to-indicator pipelines to help biodiversity and ecosystem services assessment and monitoring. InVEST, ARIES and Co$tingNature are tools for ecosystem services assessment. Marxan, Zonation, and CAPTAIN are tools for spatial prioritization.  The Figure below shows in detail those tools that can support assessment and inform multiple partners in the HJBL.

a) biophysical, economic and socio-cultural systems (adapted from Giupponi et al. 2022), and b) detail of tools for biodiversity and physical features. ARIES (ecosystem services accounts), Bon in a Box (GEO BON tools), CAPTAIN (Conservation area prioritization through artificial intelligence), CLIMSAVE (assess climate change impacts and vulnerabilities for a range of sectors), Co$tingNature (mapping supply and demand of ecosystem services), CWRVI (Canadian Water Resources Vulnerability Index to Permafrost Thaw), FGM (Projects related to Canadian fires), FireMARS (Fire monitoring and reporting tool), Geo Wetlands (Earth Observation (EO) based mapping, monitoring, inventory and assessment of wetlands), GLCM (General Landscape Connectivity Model), GlobCover (global composites and land cover maps), GLOBIO-ES (calculates the current state, trends and possible future scenarios of ecosystem services globally), GlobWetlands Africa (Toolbox for analysis and processing satellite imagery and for building models, maps and applications), GLSM (Global Land Surface Model), HydroBASINS (sub-basin boundaries at a global scale), HydroLAKES (shoreline polygons of all global lakes), IMAGE-ES (Integrated Model to Assess the Global Environment), IMPRESSIONS (extension of CLIMSAVE), InVEST (map and value the goods and services from nature), LandSyMM (Land system changes across scales), Marxant (solutions to conservation planning problems), NEST (The Northern Ecosystems Soil Temperature model), RUSLE (Revised Universal Soil Loss Equation), SolVES (Social Values for Ecosystem Services ), SWAT (Soil & Water Assessment Tool), TWIPT (The Wetland Intrinsic Potential tool), Wetland Network (Practical tools and resources to better understand and conserve wetlands, Canada), Zonation (spatial priority ranking software).

2. What is included in this review

This section is under construction!

We have included not only models to measure or assess some facet of biodiversity but physical models that can be important to indirectly monitor change of biodiversity and ecosystem services, in particular those that use remote sensing as source of data.

We focused our review on some ecosystem services in the region (carbon storage-climate stability and traditional use of biota-food security).

We acknowledge that there may be models, approaches and tools out there that escaped this review, while noting that there are some biophysical components and ecosystem services that were more explored and covered than others. For instance, water quantity and quality was barely reviewed, so partners highlighted the need to put additional efforts on this topics. Similarly, some ecosystem services were explored in major detail than others, that is carbon storage on the HJBL that offer global climate stability. However, partners also the vast spectrum of ecosystem services that the HJBL offer for locals and Canada. So, further efforts should focus on tackling these gaps and updating models, tools and other means when possible.

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3. Topics to consider when using models

This section is under construction!

Partners should clearly identify the purpose of modelling and match it with their goals, questions and objectives to protect and manage biodiversity and ecosystem services. Modelling can have multiple purposes (for more information see Tredennick et al. (2021) and Geary et al. 2020) , for instance:

The EcoCode working group from GEO BON summarizes multiple stages of modelling.

Models are needed at every stage of the Biodiversity Monitoring-to-Mitigation pathway, starting from data observation and continuing through to projection and mitigation:

  • Data observation: models are needed that can account for varying sample intensities and methods in order to assess uncertainties accurately. 

  • Building indicators: models are used to aggregate and differentially weight certain observations into aggregated diversity indicators, especially for use in monitoring trends.

  • Trend detection: usually, statistical models that accurately account for propagated errors are applied to evaluate trends of biodiversity change through time. 

  • Attribution: for attributing biodiversity change to specific drivers, models are  required that can represent expected biodiversity patterns with and without focal drivers. These models might take the form of statistical models or mechanistic models parameterized for key processes.

  • Projection: biodiversity projections can evaluate the impact of different future social and economic scenarios on future biodiversity – for instance, how different land use and climate change scenarios might affect future biodiversity loss. 

  • Mitigation: projection models can be used to test mitigation options via simulation and also incorporate additional social costs in prioritising mitigation options.

Biodiversity Modelling as Part of an Observation System (Ferrier et al. 2016)

Biodiversity modeling advances will improve predictions of nature’s contributions to people (Kass et al. 2024).

A practical guide to selecting models for exploration, inference, and prediction in ecology (Tredennick et al. 2021).

 

Biophysical and socio-economic variables, as well as, processes and patterns of ecological systems operate and vary as a function of spatial (extent and resolution) and temporal scale (e.g.,Hewitt et al 2017, Zarnetske et al 2019, Madsen & de Silva 2024, Zelnik et al 2023). It is also expected that the response to multiple anthropogenic drivers also vary in space and time (Bastazini et al. 2021), posing a challenge for model integration (Isbell et al. 2017, Weiskopf et al. 2022).

“The ecological patterns and variability we observe range from millimetres to across ocean basins and from seconds to the expanse of evolutionary history. Patterns apparent at one scale can collapse to noise when viewed from other scales, indicating that perceptions of the importance of different processes vary in a scale-dependent manner” (Hewitt et al 2010).

Let’s take a look of some figures explaining how variables, patterns and processes vary across spatial and temporal scales:

Geodiversity across scales. (a) Examples of geodiversity variables and the spatial and temporal extents at which they vary. Geodiversity encompasses abiotic components of the Earth’s critical zone, specifically the lithosphere (brown), atmosphere (red), hydrosphere (blue) and cryosphere (grey) (Natural Resources Council, 2001; Parks & Mulligan, 2010). In general, surficial geodiversity at regional to global scales remains constant over short time-frames (e.g., days to years), whereas local-scale surficial geodiversity (e.g., micro-topography and the physical and chemical properties of soil) vary over short to intermediate time-frames (e.g., years to centuries). (b) Examples of satellite remotely sensedgeodiversity (black). As point data, biodiversity data (green) are often high resolution, but are lacking in spatial and temporal extent. Networked sites, such as the National Ecological Observatory Network (NEON) and Long-Term Ecological Research (LTER) sites, provide a combination of biodiversity and geodiversity (dark green). See an interactive table with a more complete list of NASA missions and products for geodiversity at: https://bioxgeo.github.io/bioXgeo_ProductsTable/, also in Suporting Information Appendix A. Additional abbreviations are as follows: BBS = Breeding Bird Survey; FIA = forest inventory and analysis; G-LiHT = Goddard’s LiDAR hyperspectral thermal imager; GPM = global precipitation measurement mission; GRACE = gravity recovery and climate experiment; MODIS = MODerate resolution imaging spectroradiometer; SMAP = soil moisture active passive; SRTM = shuttle radar topography mission; TRMM = tropical rainfall measuring mission (Figure from Zarnetske et al 2019).

The manner in which space-time domains of entities and concepts relevant to maintaining biological diversity in forests are commonly perceived. (a) components of forests and effects modifying forests. (b) forest dwelling organisms and processes affecting them. (c) concepts related to conserving biological diversity (from Bunnell & Huggard 1999).

Measuring across spatiotemporal scales in marine systems. Spatial and temporal scales of marine dynamic and ecosystem processes, and the measurement capabilities of different observational platforms. Dynamic and/or physical processes are represented by black circles and biological and/or ecological processes by green circles. The presence of new autonomous platforms, such as Biogeochemical-Argo (BGC-Argo) floats (red line) and gliders and similar unmanned surface vehicles (USVs) (purple line), fill the cross-discipline and cross-scale observational gaps left by traditional observation methods such as remote sensing by satellite (yellow line), moorings (green line) or ship-based observations (blue line). Dashed lines indicate potential extension of an observational network (Chai et al. 2020).

Spatial and temporal scales of social processes(bold blue font) and ecological processes (italicized green font) that affect forests, many of which occur on the landscape scale (grey oval). (From Fischer 2018).

How spatial scale shapes the generation and management of multiple ecosystem services (Lindborg et al. 2017)

Understanding relationships among ecosystem services across spatial scales and over time (Qiu et al. 2018).

National ecosystem services mapping at multiple scales. The German exemplar (Rabe et al. 2016)

The supply of multiple ecosystem services requires biodiversity across spatial scales (Le provost et al. 2022)

We align the Living Planet Index (LPI) source-based categories of stressors with a classical view of ecological scales and the target metrics at each scale. The framework (and models, see below) highlights targets and ecological scales that coalesce the impacts of stressors by what they do, not what they are. For example, pollution and climate stressors can both generate direct effects on the abundance of species by killing individuals, but also via trait-mediated effects acting on growth and reproduction. As different ecological scales are connected by higher-order interactions, outputs at higher and lower levels of ecological organization can emerge. Note that we define the effects of a stressor at the individual scale as those that act within, rather than between, individuals. Thus, a harvesting process, such as hunting, can have individual-scale effects by altering stress hormone levels in individuals, which has consequences for demography and therefore the population. However, hunting primarily acts by killing individual animals or plants, which directly affects properties of the population (abundance/biomass) not properties of the individual. Note that this figure is a first attempt designed to illustrate our framework and is by no means comprehensive. (From Simmons et al. 2021).

Re-evaluating global assessments using local datasets

For instance, Dinerstein et al. (2020), identified global areas providing co-benefits by protecting biodiversity and carbon stocks aimed to secure global climate stability (Global Safety Net, GSN). However, Finkelstein et al. (2023) applied the same approach in Ontario (Canada), using local data sets, and found that “when region-specific data are incorporated, Ontario is even more significant than what is shown in the GSN, especially in terms of carbon stocks in forested and open peatlands.”  Zhu et al. (2021) went further and addressed the issue of spatial scales in Asia, arguing that a “framework should be capable of identifying priorities at each scale (regional, biome, and national)”. They proposed “a stepwise approach based on scalable priorities at regional, biome, and national levels that can complement potential Convention on Biological Diversity targets of protecting 30% land in the post-2020 global biodiversity framework.” 

Integrating multiple knowledge systems

Fire regimes characterization using remote sensing (Hanes et al. 2018, Coops et al. 2018) have been used to inform management activities for government agencies (Erni et al. 2019) and along with Indigenous land stewardship have provided tools to advance proactive fire mitigation (Copes et al. 2022).

Developing multi-scale scenarios

Rosa et al. (2017) have proposed a two-step strategy to develop a new generation of scenarios within the IPBES (2016) framework, as follows: ?” (i) extend existing global scenarios developed by the climate-science community, by modelling impacts on biodiversity and ecosystem services (Fig. 1a); and (ii) make an ambitious effort to create a set of multiscale scenarios of desirable ‘nature futures’, based on the perspectives of different stakeholders, taking into account goals for both human development and nature stewardship (Fig. 1b).” (See figure here).

Linking the influence and dependence of people on biodiversity across scales

Isbell et al. (2017)

Multiscale dependance has been documented widely:

Top-down approaches are driven by policy mandates and/or directives, requiring changes in socioeconomic systems (e.g., SDGs and CBDs goals and targets) and designed to force behavioral change through policy.

Bottom-up approaches influence policy through collective behavior (individual actions that can propel massive impact when adopted by communities) and/or motivate research to answer questions at local scales.

Ding et al. (2023)offers a good description of issues emerging the lack of integration between bottom-up and top-down approaches, as follow: Currently, top-down approaches originating from hierarchical government structures [2] have created widespread and immediate conservation mandates and are thus widely applied in large-scale ecological restoration programs [3]. Specifically, a higher-level government (“buyer”), like the central government, sets nationwide ecological restoration goals based on the biophysical processes of land conversion. The lower-level government or local-scale organizations, such as local governments, are tasked with achieving these goals through negotiations with the participants (“seller”), like landowners, to achieve certain conservation mandates [4], [5], [6]. However, spatial mismatches in implementation often occur on a broad scale, where decisions are based on coarse scale information and the local scale, and the implementation of on-the-ground restoration is impacted by resource limitations and social complexity [7], [8], [9]. This mismatch often results in the failure of large-scale restoration planning due to a failure to characterize social–ecological interactions affecting stakeholder behavior during the implementation processes and thus, fails to achieve the desired goals [10], [11]. To minimize implementation conflicts, large-scale restoration planning should incorporate a comprehensive analysis of social–ecological interactions, including socioeconomic constraints, participant willingness [12], [13], and decision-maker preferences [11].

Figure link and description from Ding et al. (2023) Integrated framework for top-down and bottom-up approaches within social–ecological systems based on a large-scale ecological restoration program. (a) An analytical framework was established to integrate top-down (focusing on ecological characteristics) and bottom-up (considering behaviors of stakeholders) approaches to increase practicality and efficiency of large-scale ecological restoration programs. (b) A three-step bottom-up approach to integrate impacts of stakeholder behaviors from previous fine-scale research into large-scale ecological programs.

Coupling socio-ecological interactions at local levels with large scale planning strategies

See Ding et al. (2023)

Core principles

Eicken et al. (2021) “identify core principles central to such improved links: matching observing program aims, scales, and ability to act on information; matching observing program and community priorities; fostering compatibility in observing methodology and data management; respect of Indigenous intellectual property rights and the implementation of free, prior, and informed consent; creating sufficient organizational support structures; and ensuring sustained community members’ commitment”.

Codesign, comanagement, and coproduction

“..the implementation of principles of codesign, comanagement, and coproduction is facilitated greatly by focusing on pressing societal problems at a scale that intersects interests of both local communities in a particular region and large-scale observing efforts” (Eicken et al. 2021 ).

Integrating bottom -up and top-down biodiversity metrics

“Top-down intactness metrics (e.g., Mean Species Abundance) can be valuable for generating high-level or first-tier assessments of impact risk but do not provide sufficient precision or guidance for companies, regulators, or third-party assessors. New metrics based on bottom-up assessments of biodiversity (e.g., the Species Threat Abatement and Restoration metric) can accommodate spatial variation of biodiversity and provide more specific guidance for actions to avoid, reduce, remediate, and compensate for impacts and to identify positive opportunities”….. “The 2 approaches in combination may provide a clear path for businesses to identify the most effective actions to reduce impacts. This will be crucial for compliance with emerging disclosure frameworks, such as the Taskforce on Nature-related Financial Disclosures (2022), and for setting science-based targets for nature (Science Based Targets, 2023)”. (Hawkins et al 2023).

Top-down approaches are usually the first approximation in model implementation, scenario assessment (e.g., Moss et al. 2010, Meinshause et al. 2020,  and Popp et al. 2017), and tools’ development (e.g., Map of Life, InVEST, GLOBIO-ES).

For bottom-up approahces, GLOBIOM is “used to  analyze the competition for land use between agriculture, forestry, and bioenergy, which are the main land-based production sectors. The model is built following a bottom-up setting based on detailed grid-cell information, providing the biophysical and technical cost information”.

There is growing interest in evaluating top-down approaches with local and regional data sets (Siabi et al. 2023), economic sectors (Calheiros et al. 2023), and integrating top-down and bottom-up approaches (Ding et al. 2022, Eicken et al. 2021) .

Lynam et al. (2016) show the interaction between top-down and bottom-up control in marine food webs; and, Gaymer et al. (2014) show key lessons in integrating top-down and bottom-up approaches to stakeholder and community engagement in the planning and implementation of marine protected areas.

Riva et al. (2023) for complexity

Drivers of biodiversity

Biodiversity facets interacting at multiple scales

Biodiversity and ecosystem services

Biodiversity responding to multiple drivers

  • Biodiversity and climate change (e.g., Shivanna 2022)

  • Biodiversity and land cover and land use change

  • Biodiversity and CC+LCLUC

Biodiversity loss and its effects on ecosystem (services) stability

Any region in the world is a complex mix of biophysical, socioeconomic and cultural systems, interacting at multiple spatial and temporal scales. Models can capture some aspect of a particular system, for instance predicting plant biomass accumulation (e.g., Chen et al. 2018), prediciting species distributions (Pinto & Cavender 2021), identifying areas of vulnerability to climate change (Carroll et al. 2015) or predicting species’ range shifts under climate change (Antão et al. 2022).

Combining models can provide more insights on the phenomenon under study and capture the strength of each model within the pool of models. Moreover, combining models from different systems might provide a more accurate assessment of the interactions of these systems by capturing multi-partners needs and expectations, integrating multiple features of the biophysical and socio-economic systems and assessing multiple scenarios. For instance, co-benefits of biodiversity and carbon storage have been recently explored (Soto-Navarro et al. (2020), Dinerstein et al. (2020), Finkelstein et al. (2023) and Zhu et al. (2021) and Integrated Assessment Models (IAMs) are spatially explicit and mathematical models, usually global in scope, sector oriented (e.g., Krey 2014) and originally designed to evaluate climate change scenarios (e.g., SSPs, IPCC 2021, FurhmaN et al. 2019) and SDGs (van Soest et al. 2019). 

Integrating facets within systems (e.g., Biodiversity facets)

Coupling models and tools

Some analyses require coupling several models to accomplish their objectives. For instance,

  • projecting land cover and land use change (LCLUC) to assess ecosystem services under multiple scenarios requires coupling Multi-Objective Programming (MOP) to  optimized solutions for each land use type subject to a series of constraints specified by a given scenario  and the Dynamic Conversion of Land Use and its Effects (Dyna-CLUE) to allocate the predicted land use changes to grid cells following a bottom-up process (Wang et al. 2018).

  • Li et al. (2023) have used the “Patch-generating Land Use Simulation (PLUS) to assess the features of land utilization conversion and forecasted land utilization under three development patterns in 2030” and then the “InVEST model to estimate changes in carbon storage trends under three development scenarios in 2000, 2010, 2020, and 2030 and the impact of socioeconomic and natural factors on carbon storage”. 

There are also strategies to ensemble models to reduce uncertainty. For instance, Hooftman et al. (2022) and Willcock et al (2023) evaluated the effect of ensemble models to reduce certainty gaps in spatially explicit ecosystem services. (e.g,  using multiple ecosystem accounting platforms such as InVEST, ARIES, and Co$tingNature).

Stralberg et al. (2015) used multiple GCMs projections and SDMs to evaluate signal vs. noise in the response of bird abundance to climate change. They found that for 58% of 80 boreal songbird species over the next 30 years—increasing to 88% of species by the end of the century—the climate change “signal” in projections of abundance was greater than the “noise” generated by uncertainty due to a combination of sampling error, variable selection, and choice of global climate model (GCM)” (Stralberg et al. 2015).

Weiskopf et al. (2022)

Cross-sectoral integration

Frieler et al. (2015)

Integrated Assessment Models (IAMs)

Model integration seems to offer a pathway to understand complex systems and inform decision making. However, current models are global, make multiple assumptions and can generate high spatial and temporal uncertainty. Integrated Assessment Models (IAMs) have been developed to respond to this need of integration. Originally, IAMs were developed to evaluate multiple scenarios of climate change, mainly focused on economic sectors (e.g., energy) aimed to inform alternative policies in GHG emissions reduction.  Recently, they have been extended to other sectors and customized to include land cover and land use change, and some biodiversity facets and ecosystem services, as well. However, their implementation is limited.

“Model intercomparisons bring together different communities of practice for comparable and complementary modeling, in order to improve the comprehensiveness of the subject modeled, and to estimate uncertainties associated with scenarios and models (Frieler et al., 2015)” (Kim et al. 2018)

Intercomparison of biodiversity and ecosystem services models using harmonized scenarios (BES-SIM)

“The goals of BES-SIM are (1) to project the global impacts of land-use and climate change on biodiversity and ecosystem services (i.e., nature’s contributions to people) over the coming decades, compared to the 20th century, using a set of common metrics at multiple scales, and (2) to identify model uncertainties and research gaps through the comparisons of projected biodiversity and ecosystem services across models” (Kim et al. 2018) .

Model intercomparison across multiple scales

“Mechanistic and process-based model intercomparisons can act as a platform for systematically testing the performance of different encoded mechanisms and processes across scales not considered during model development. For instance, intercomparisons could determine whether existing mechanistic IBMs designed at the field scale can predict meta-population dynamics across isolated habitat patches at the landscape scale, or whether process-based MCMs can predict local species population dynamics” (Johnston et al. 2024).

Example systematic model intercomparison framework for testing existing mechanistic and process-based ecological models (a, d, g, j) across multiple spatiotemporal scales (b, e, h, k) and levels of biological organization (c, f, i, l). This example illustrates a comparison between an energy budget IBM (a) developed at the field scale (b) to predict population dynamics (c), dispersal kernel metapopulation model (d) to predict landscape-scale (e) meta-population dynamics (f), metacommunity model with species-specific population growth rates (r) across abiotic gradients (g) and pairwise interaction strengths to predict regional-scale (h) species interaction networks (i) and trophic ecosystem model (j) to predict national to global (k) community dynamics (l). All existing models would be simulated at each scale and model predictions compared to multiple empirical patterns at different levels of biological organization according to a standardized pattern-oriented modelling (POM) evaluation procedure (m) and computational tractability monitored, for example according to computer processing unit (CPU) time across scales (n). Model comparisons (m, n) would provide valuable information about important mechanisms and processes at specific scales and act as a platform for cross-scale ecological modelling, for example by comparing alternative ensemble models (grey line in m, n) that address scale-explicit model constraints. (Form Johnston et al. 2024).

Couple Model Intercomparison Project (CMIP)

CMIP is a framework for climate models to analise, validate and improve Global Circulation Models (GCMs).

  • It is “coupled” because all the climate models are coupled atmosphere and ocean GCMS.

  • It is “intercomparison” because CMIP brings together and into line all climate models from multiple modelling centres.
    multiple generations of CMIP have been developed (see more here and PCMDI)

“Global coupled climate models are elaborate numerical/physical formulations of the atmosphere, ocean, cryosphere, and land which are”coupled” together and interact to simulate the three-dimensional distribution of the climate over the globe” (Meehl et al. 2011).

The CMIP6 multi-model ensembles of temperature and precipitation, near-surface wind speed, sea ice concentration, sea ice thickness, and snow depth for each Shared Socio-economic Pathway (SSP).

See a description in the Government of Canada webs

Biodiversity facets

The EcoCode working group from GEO BON wants to create a Coupled Model Intercomparison Project among biodiversity models

To do:

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4. Guidance for action

Model planning

Deploy a suite of interactive and user-focused of models and tools for biodiversity and ecosystem services based on the Toolbox presented in this website. Existing tools and initiatives can inspire the design and structure of an operational system in the HJBL (e.g., PERSEUS, CCADI, CreeGeo Hub, Bon in a Box).

Identify spatial conservation priorities using traditional knowledge (e.g., Noble et al. 2020). A participatory process with multiple partners should identify the main questions and expectations, potential scenarios, and multi-objectives for the HJBL. This will guide models and tool selection, as well as data sets needed.

Model implementation

Start with exploratory analysis (e.g.,  an assessment of biodiversity and ecosystem services change in the region, see Dang et al. 2020) and then develop more complex causal models and scenarios to support spatial planning and prioritization. Use time-series observations across spatial location to guide the calibration and validation of biodiversity and ecosystem services models.

Model integration

There is a need to develop existing models to provide robust predictions for biodiversity ( e.g., Weiskopf et al. 2022, Hill et al. 2016) and nature’s contribution to people (Kass et al. 2024). Some of the key actions should include:

  • Identify the scales at which different processes operate and operationalize models to capture these scale-specific processes explicitly (e.g. dispersal, flows of water).

  • Integrate modeling and monitoring data (see some guidelines from Honrado et al. 2016) ; the SCALES project offer a relevant methodological approach).

  • Expand models to include multiple dimensions of biodiversity (e.g., genetic diversity) and ecosystem services (e.g., cultural services) simultaneously.

  • Integrate of top-down and bottom-up approaches (e.g., biodiversity models (Pollock et al. 2020); global environmental assessments (Pereira et al. 2021), wildlife management (Gandiwa et al. 2013),  ecosystem services (Sieben et al. 2018), climate adaptation (Girard et al. 2014, Pulido et al. 2022, ), and assessing prioritization scenarios to protect biodiversity (Eckert et al. 2022). 

  • Integrate biodiversity, ecosystem function, and ecosystem services. For instance, (Isbell et al. 2017, Weiskopf et al. 2021) suggested using the following:

    • empirical data on biodiversity-ecosystem function relationships to link biodiversity model output directly to ecosystem services models (Mori et al. 2021).

    • network models to assess joining sustainability of biodiversity and ecosystem services at local to regionals scales (Gonzalez et al. 2017).

    • Operationalize methods and tools available for multi-objective conservation planning that can be applied in the HJBL (see next sections), including addressing spatially-explicit uncertainties in all stages of the process (e.g., data inputs, predictions, etc.) (Kim et al. 2018, Geary et al. 2020, Myers et al. 2021).